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One of recent trends [30, 31, 14] in network architec- ture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more ef- ficient than a large kernel, given the same computational complexity.
Fast high-dimensional filtering using the permutohedral lattice
A. Adams, J. Baek, and M. A. Davis · 2010
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The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
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Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 2011
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Efficient inference in fully connected crfs with gaussian edge potentials
V. Koltun · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Object detectors emerge in deep scene cnns
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2014
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Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling
V. Badrinarayanan, A. Handa, and R. Cipolla · 2015
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
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Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
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The pascal visual object classes challenge: A retrospective
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2015
Cited alongside, same era.
Semantic image segmentation via deep parsing network
Z. Liu, X. Li, P. Luo, C.-C. Loy, and X. Tang · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Feedforward semantic segmentation with zoom-out features
M. Mostajabi, P. Yadollahpour, and G. Shakhnarovich · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
The fast bilateral solver
J. T. Barron and B. Poole · 2016
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Fast, exact and multi-scale inference for semantic image segmentation with deep gaussian crfs
S. Chandra and I. Kokkinos · 2016
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Laplacian pyramid reconstruction and refinement for semantic segmentation
G. Ghiasi and C. C. Fowlkes · 2016
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Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Cited alongside, same era.
Holistically-nested edge detection
S. Xie and Z. Tu · 2015
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2015
Cited alongside, same era.
Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Learning sparse high dimensional filters: Image filtering, dense crfs and bilateral neural networks
V. Jampani, M. Kiefel, and P. V. Gehler · 2016
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Convolutional scale invariance for semantic segmentation
I. Krešo, D. Čaušević, J. Krapac, and S. Šegvić · 2016
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Efficient piecewise training of deep structured models for semantic segmentation
G. Lin, C. Shen, A. van den Hengel, and I. Reid · 2016
Later among the works it cites.
Fully convolutional networks for semantic segmentation
E. Shelhamer, J. Long, and T. Darrell · 2016
Later among the works it cites.
Objectness-aware semantic segmentation
Y. Wang, J. Liu, Y. Li, J. Yan, and H. Lu · 2016
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High-performance semantic segmentation using very deep fully convolutional networks
Z. Wu, C. Shen, and A. v. d. Hengel · 2016
Later among the works it cites.